You can’t always get what you want: fish, sensors and fishermen
Bibliographic record
Abstract
Fishing is an important recreational activity in Trentino with an estimated economic impact of approximately €1.5 million per year in seasonal and daily fishing licenses, without considering revenue generated by equipment, participation in fishing tournaments, hospitality, etc. In this region, anglers’ expectations are geared towards trout (Salmo truta L) and fishing associations regularly stock brown trout to meet this demand. For higher altitude lakes however, stocking with brown trout is no longer permitted and provincial fish management plans require replacing non-native species such as brown trout and rainbow trout (Oncorhynchus mykiss) with native Arctic char (Salvelinus alpinus). This has led to complaints from stakeholders (resident and visiting anglers, wardens, associations) about lower catches with loss of revenue for anglers’ associations. While lower altitude lakes are often repeatedly stocked with brown trout, they do not always provide suitable habitats for salmonids. This is often the case where upstream water abstraction changes the hydrological regime of a lake that historically supported a trout population. Temperature sensors, such as iButtons and HOBOs, are an economical educational tool useful to illustrate the compatibility of seasonal water temperature with salmonid survival. Examples from Lakes Campo and Roncone are given.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".